Recurrent infomax generates cell assemblies, neuronal avalanches, and simple cell-like selectivity.
Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.
Tanaka
Takuma
T
Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University, Kyoto 606-8501, Japan. ttakuma@mbs.med.kyoto-u.ac.jp
Kaneko
Takeshi
T
Aoyagi
Toshio
T
D000465:Algorithms; D000818:Animals; D002490:Central Nervous System / Q000166:cytology / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*